The present invention relates to image processing, and in particular, to the processing of medical images for subsequent evaluation.
A number of techniques are currently available for the imaging of internal parts of the body, e.g., X-ray imaging, magnetic resonance imaging (MRI), ultrasonic imaging, computer aided tomography (CAT), positron emission tomography (PET), etc. In most cases, a "hard" copy of the image is produced and is directly evaluated by a clinician who, in some cases, may be required to evaluate the size or shape of an object in the image to determine an abnormality.
It has been found that it would be desirable to be able to evaluate the images automatically, particularly when the evaluation is one of size or shape.
However, in order to perform an automated evaluation, it is necessary to first obtain a faithful representation of the object to be evaluated from the image of the object. Many problems are encountered in this regard. For example, it is difficult to obtain a complete outline or representation of the desired object when part of the object consists of very low intensity portions of the image. Moreover, these images are by their nature filled with clutter or noise that interferes with the evaluation.
These problems associated with clutter elimination and outline processing are more complex than in the case of pattern recognition. In pattern recognition, one is required to detect an object that is selected from a finite set of predetermined and known shapes. In the case of medical imaging although the general shape or size of the target object, i.e., a fetal skull or an ovary, in the medical image is known, the precise characteristics are virtually infinite in possibilities and are, therefore, unknown. In fact, since it often is the variation in size or shape that is being evaluated, the act of generalizing the shape would result in the loss of information. Therefore, it is necessary to process and enhance the image to determine the accurate size or shape of the actual object.